scholarly journals Raspberry Pi Based Intelligent Wireless Sensor Node for Localized Torrential Rain Monitoring

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Zhaozhuo Xu ◽  
Fangling Pu ◽  
Xin Fang ◽  
Jing Fu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Node ◽  
Heavy Rain ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Raspberry Pi ◽  
Support Vector ◽  
Wireless Sensor Node ◽  
Torrential Rain

Wireless sensor networks are proved to be effective in long-time localized torrential rain monitoring. However, the existing widely used architecture of wireless sensor networks for rain monitoring relies on network transportation and back-end calculation, which causes delay in response to heavy rain in localized areas. Our work improves the architecture by applying logistic regression and support vector machine classification to an intelligent wireless sensor node which is created by Raspberry Pi. The sensor nodes in front-end not only obtain data from sensors, but also can analyze the probabilities of upcoming heavy rain independently and give early warnings to local clients in time. When the sensor nodes send the probability to back-end server, the burdens of network transport are released. We demonstrate by simulation results that our sensor system architecture has potentiality to increase the local response to heavy rain. The monitoring capacity is also raised.

scholarly journals Concurrent Data Message Transmissions for Interference of Illegal Overhearing in Wireless Sensor Networks

2018 ◽  
Vol 210 ◽  
pp. 03011
Author(s):  
Masahiro Okuri ◽  
Hiroaki Higaki
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Node ◽  
Wireless Transmission ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Transmission Range ◽  
Wireless Sensor Node ◽  
Wireless Sensor Nodes ◽  
Wireless Nodes

In wireless sensor networks, data messages containing sensor data achieved by a sensor module in a wireless sensor node is transmitted to a stationary wireless sink node along a wireless multihop transmission route in which wireless sensor nodes themselves forward the data messages. Each intermediate wireless sensor node broadcast data messages in its wireless transmission range to forward them to its next-hop intermediate wireless sensor node. Hence, eavesdropper wireless nodes within the wireless transmission range easily overhear the data messages. In order to interfere with the eavesdropper wireless nodes illegally overhearing the data messages in transmission, wireless sensor nodes whose wireless transmission ranges overlap and their next-hop intermediate wireless sensor nodes are out of the wireless transmission ranges each other forward data messages in transmission concurrently and cause collisions between these two data messages at any possible eavesdropper wireless nodes intentionally. To enhance regions where concurrently forwarded data messages intentionally collide to prevent their overhearing and to realize concurrent forwarding of data messages, this paper designes an algorithm for TDMA transmission slot assignments for more opportunities to interfere the eavesdropper wireless nodes.

scholarly journals Energy Efficiency Analysis of LoRa and ZigBee Protocols in Wireless Sensor Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 2836-2849
Author(s):  
K. Raghava Rao ◽  
D. Sateesh Kumar ◽  
Mohiddin Shaw ◽  
V. Sitamahalakshmi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Power Consumption ◽  
Low Power ◽  
Sensor Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Node ◽  
Wireless Sensor Nodes ◽  
Iot Applications

Now a days IoT technologies are emerging technology with wide range of applications. Wireless sensor networks (WSNs) are plays vital role in IoT technologies. Construction of wireless sensor node with low-power radio link and high-speed processors is an interesting contribution for wireless sensor networks and IoT applications. Most of WSNs are furnished with battery source that has limited lifetime. The maximum operations of these networks require more power utility. Nevertheless, improving network efficiency and lifetime is a curtail issue in WSNs. Designing a low powered wireless sensor networks is a major challenges in recent years, it is essential to model its efficiency and power consumption for different applications. This paper describes power consumption model based on LoRa and Zigbee protocols, allows wireless sensor nodes to monitor and measure power consumption in a cyclic sleeping scenario. Experiential results reveals that the designed LoRa wireless sensor nodes have the potential for real-world IoT application with due consideration of communicating distance, data packets, transmitting speed, and consumes low power as compared with Zigbee sensor nodes. The measured sleep intervals achieved lower power consumption in LoRa as compared with Zigbee. The uniqueness of this research work lies in the review of wireless sensor node optimization and power consumption of these two wireless sensor networks for IoT applications.

scholarly journals A Data-Based Fault-Detection Model for Wireless Sensor Networks

2019 ◽  
Vol 11 (21) ◽  
pp. 6171 ◽  
Author(s):  
Jangsik Bae ◽  
Meonghun Lee ◽  
Changsun Shin
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Detection ◽  
Data Transmission ◽  
Sensor Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Sensors And Actuators ◽  
Detection Model ◽  
Management Platform

With the expansion of smart agriculture, wireless sensor networks are being increasingly applied. These networks collect environmental information, such as temperature, humidity, and CO2 rates. However, if a faulty sensor node operates continuously in the network, unnecessary data transmission adversely impacts the network. Accordingly, a data-based fault-detection algorithm was implemented in this study to analyze data of sensor nodes and determine faults, to prevent the corresponding nodes from transmitting data; thus, minimizing damage to the network. A cloud-based “farm as a service” optimized for smart farms was implemented as an example, and resource management of sensors and actuators was provided using the oneM2M common platform. The effectiveness of the proposed fault-detection model was verified on an integrated management platform based on the Internet of Things by collecting and analyzing data. The results confirm that when a faulty sensor node is not separated from the network, unnecessary data transmission of other sensor nodes occurs due to continuous abnormal data transmission; thus, increasing energy consumption and reducing the network lifetime.

scholarly journals Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models

2014 ◽  
Vol 5 (1) ◽  
pp. 20-45 ◽  
Author(s):  
Osman Salem ◽  
Alexey Guerassimov ◽  
Ahmed Mehaoua ◽  
Anthony Marcus ◽  
Borko Furht
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Patient Data ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Smart Devices ◽  
Support Vector ◽  
Processing Unit

This paper details the architecture and describes the preliminary experimentation with the proposed framework for anomaly detection in medical wireless body area networks for ubiquitous patient and healthcare monitoring. The architecture integrates novel data mining and machine learning algorithms with modern sensor fusion techniques. Knowing wireless sensor networks are prone to failures resulting from their limitations (i.e. limited energy resources and computational power), using this framework, the authors can distinguish between irregular variations in the physiological parameters of the monitored patient and faulty sensor data, to ensure reliable operations and real time global monitoring from smart devices. Sensor nodes are used to measure characteristics of the patient and the sensed data is stored on the local processing unit. Authorized users may access this patient data remotely as long as they maintain connectivity with their application enabled smart device. Anomalous or faulty measurement data resulting from damaged sensor nodes or caused by malicious external parties may lead to misdiagnosis or even death for patients. The authors' application uses a Support Vector Machine to classify abnormal instances in the incoming sensor data. If found, the authors apply a periodically rebuilt, regressive prediction model to the abnormal instance and determine if the patient is entering a critical state or if a sensor is reporting faulty readings. Using real patient data in our experiments, the results validate the robustness of our proposed framework. The authors further discuss the experimental analysis with the proposed approach which shows that it is quickly able to identify sensor anomalies and compared with several other algorithms, it maintains a higher true positive and lower false negative rate.

scholarly journals Performance Analysis of Q-Leach Algorithm in WSN

2020 ◽  
Vol 5 (10) ◽  
pp. 1-4
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Data Transmission ◽  
Short Range ◽  
Sensor Node ◽  
Life Time ◽  
Time Span ◽  
Sensor Nodes ◽  
Wireless Sensor

Sensor nodes are exceedingly energy compelled instrument, since it is battery operated instruments. In wsn network, every node is liable to the data transmission through the wireless mode [1]. Wireless sensor networks (WSN) is made of a huge no. of small nodes with confined functionality. The essential theme of the wireless sensor network is energy helpless and the WSN is collection of sensor. Every sensor terminal is liable to sensing, store and information clan and send it forwards into sink. The communication within the node is done via wireless network [3].Energy efficiency is the main concentration of a desining the better routing protocol. LEACH is a protocol. This is appropriate for short range network, since imagine that whole sensor node is capable of communication with inter alia and efficient to access sink node, which is not always correct for a big network. Hence, coverage is a problem which we attempt to resolve [6]. The main focus within wireless sensor networks is to increase the network life-time span as much as possible, so that resources can be utilizes efficiently and optimally. Various approaches which are based on the clustering are very much optimal in functionality. Life-time of the network is always connected with sensor node’s energy implemented at distant regions for stable and defect bearable observation [10].

Efficient Energy Conservation and Faulty Node Detection on Machine Learning-Based Wireless Sensor Networks

2021 ◽  
Vol 13 (2) ◽  
pp. 1-20
Author(s):  
Amarasimha T. ◽  
V. Srinivasa Rao
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Communication ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Support Vector ◽  
Faulty Node

Wireless sensor networks are used in machine learning for data communication and classification. Sensor nodes in network suffer from low battery power, so it is necessary to reduce energy consumption. One way of decreasing energy utilization is reducing the information transmitted by an advanced machine learning process called support vector machine. Further, nodes in WSN malfunction upon the occurrence of malicious activities. To overcome these issues, energy conserving and faulty node detection WSN is proposed. SVM optimizes data to be transmitted via one-hop transmission. It sends only the extreme points of data instead of transmitting whole information. This will reduce transmitting energy and accumulate excess energy for future purpose. Moreover, malfunction nodes are identified to overcome difficulties on data processing. Since each node transmits data to nearby nodes, the misbehaving nodes are detected based on transmission speed. The experimental results show that proposed algorithm provides better results in terms of reduced energy consumption and faulty node detection.

Nature-Inspired Algorithms in Wireless Sensor Networks

2019 ◽  
pp. 246-275
Author(s):  
Ajay Kaushik ◽  
S. Indu ◽  
Daya Gupta
Keyword(s):  
Wireless Sensor Networks ◽  
Big Data ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Sensor Node ◽  
Optimization Problem ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Nature Inspired Algorithms

Wireless sensor networks (WSNs) are becoming increasingly popular due to their applications in a wide variety of areas. Sensor nodes in a WSN are battery operated which outlines the need of some novel protocols that allows the limited sensor node battery to be used in an efficient way. The authors propose the use of nature-inspired algorithms to achieve energy efficient and long-lasting WSN. Multiple nature-inspired techniques like BBO, EBBO, and PSO are proposed in this chapter to minimize the energy consumption in a WSN. A large amount of data is generated from WSNs in the form of sensed information which encourage the use of big data tools in WSN domain. WSN and big data are closely connected since the large amount of data emerging from sensors can only be handled using big data tools. The authors describe how the big data can be framed as an optimization problem and the optimization problem can be effectively solved using nature-inspired algorithms.

A Node Authentication Model in Wireless Sensor Networks With Locked Cluster Generation

2021 ◽  
pp. 236-250
Author(s):  
C. R. Bharathi ◽  
Alapati Naresh ◽  
Arepalli Peda Gopi ◽  
Lakshman Narayana Vejendla
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Node ◽  
Cluster Formation ◽  
Cluster Head ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Generation ◽  
Secure Network

In wireless sensor networks (WSN), the majority of the inquiries are issued at the base station. WSN applications frequently require collaboration among countless sensor nodes in a network. One precedent is to persistently screen a region and report occasions. A sensor node in a WSN is initially allocated with an energy level, and based on the tasks of that sensor node, energy will be reduced. In this chapter, two proposed methods for secure network cluster formation and authentication are discussed. When a network is established then all the nodes in it must register with cluster head and then authentication is performed. The selection of cluster head is done using a novel selection algorithm and for authenticating the nodes. Also, a novel algorithm for authentication is used in this chapter. The validation and authorization of nodes are carried over by managing the keys in WSN. The results have been analyzed using NS2 simulator with an aid of list of relevant parameters.

scholarly journals Bioinspired Evolutionary Algorithm Based for Improving Network Coverage in Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Mohammadjavad Abbasi ◽  
Muhammad Shafie Bin Abd Latiff ◽  
Hassan Chizari
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evolutionary Algorithm ◽  
Sensor Node ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Target Area ◽  
Network Coverage ◽  
Detection Model

Wireless sensor networks (WSNs) include sensor nodes in which each node is able to monitor the physical area and send collected information to the base station for further analysis. The important key of WSNs is detection and coverage of target area which is provided by random deployment. This paper reviews and addresses various area detection and coverage problems in sensor network. This paper organizes many scenarios for applying sensor node movement for improving network coverage based on bioinspired evolutionary algorithm and explains the concern and objective of controlling sensor node coverage. We discuss area coverage and target detection model by evolutionary algorithm.

scholarly journals Cryptanalysis and Improvement of a Privacy-Preserving Three-Factor Authentication Protocol for Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4625 ◽  
Author(s):  
Km Renuka ◽  
Sachin Kumar ◽  
Saru Kumari ◽  
Chien-Ming Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Node ◽  
Denial Of Service ◽  
Authentication Protocol ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Strong Base ◽  
Gateway Node ◽  
Three Factor

Wireless sensor networks (WSNs) are of prominent use in unmanned surveillance applications. This peculiar trait of WSNs is actually the underlying technology of various applications of the Internet of Things (IoT) such as smart homes, smart cities, smart shopping complexes, smart traffic, smart health, and much more. Over time, WSNs have evolved as a strong base for laying the foundations of IoT infrastructure. In order to address the scenario in which a user wants to access the real-time data directly from the sensor node in wireless sensor networks (WSNs), Das recently proposed an anonymity-preserving three-factor authentication protocol. Das’s protocol is suitable for resource-constrained sensor nodes because it only uses lightweight cryptographic primitives such as hash functions and symmetric encryption schemes as building blocks. Das’s protocol is claimed to be secure against different known attacks by providing formal security proof and security verification using the Automated Validation of Internet Security Protocols and Applications tool. However, we find that Das’s protocol has the following security loopholes: (1) By using a captured sensor node, an adversary can impersonate a legal user to the gateway node, impersonate other sensor nodes to deceive the user, and the adversary can also decrypt all the cipher-texts of the user; (2) the gateway node has a heavy computational cost due to user anonymity and thus the protocol is vulnerable to denial of service (DoS) attacks. We overcome the shortcomings of Das’s protocol and propose an improved protocol. We also prove the security of the proposed protocol in the random oracle model. Compared with the other related protocols, the improved protocol enjoys better functionality without much enhancement in the computation and communication costs. Consequently, it is more suitable for applications in WSNs

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